Numerous scientific and engineering applications require solutions to boundary value
problems (BVPs) involving elliptic partial differential equations, such as the Laplace or …

Despite significant advancements in Neural Radiance Fields (NeRFs), the renderings may
still suffer from aliasing and blurring artifacts, since it remains a fundamental challenge to …

We introduce a Monte Carlo method for computing derivatives of the solution to a partial
differential equation (PDE) with respect to problem parameters (such as domain geometry or …

The idea of using a neural network to represent continuous vector fields (ie, neural fields)
has become popular for solving PDEs arising from physics simulations. Here, the classical …

Partial differential equations can model diverse physical phenomena including heat
diffusion, incompressible flows, and electrostatic potentials. Given a description of an …

This paper presents a method to leverage arbitrary neural network architecture for control
variates. Control variates are crucial in reducing the variance of Monte Carlo integration, but …

In recent years, Monte Carlo PDE solvers have garnered increasing attention in computer
graphics, demonstrating value across a wide range of applications. Despite offering clear …

Herein, we present a novel method that utilizes neural networks to improve solution
generation from Monte Carlo Partial Differential Equation solvers. Current Monte Carlo PDE …